For the SemEval shared tasks on CQA, several authors used complex recurrent and convolutional neural network architectures [1]}, [2]}. For example, [2]} used a convolutional neural network in combination with feature vectors representing lexical, syntactic, and semantic similarity as well as tree kernels. Their performance was slightly lower than the best system (SemEval-Best for 2016 in Table REF ). The best system used lexical and semantic similarity measures in combination with a ranking model based on support vector machines (SVMs) [4]}, [5]}. Both systems are harder to implement and train than the model we propose here.
For SemEval-17, [5]}, the winning team used distributed representations of words, knowledge graphs and frames from FrameNet [7]} as some of their features, and used SVMs for ranking.
